Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method comprising: receiving, by a computing device and using an application program interface (API), analytics data at a feeder level of a utility transmission and distribution system; generating, by the computing device, a confidence score for a feeder based on the analytics data at the feeder level; receiving, by the computing device using the API, analytics data at a transformer level of the utility transmission and distribution system; generating, by the computing device, a confidence score for a transformer associated with the feeder based on the analytics data at the transformer level and the confidence score of the feeder; receiving, by the computing device using the API, analytics data for customers associated with the transformer; generating, by the computing device, confidence scores for the customers based on the analytics data and the confidence score of the transformer; determining, by the computing device, an effectiveness of a demand response (DR) program based on the confidence scores, wherein the DR program is an energy savings event for utility load management; purchasing energy based on the confidence scores; and outputting, by the computing device, information regarding the confidence scores and the effectiveness of the DR program, wherein the confidence scores of the customers indicate an estimated amount of energy saved by the customers during the DR program, and wherein the confidence scores of the feeder and the transformer indicate a potential cost savings for reducing energy transmitted via the feeder or transformer and the effectiveness of the DR program indicates energy and costs saved by the DR program, the method further comprising using the confidence scores to perform all of: selecting a target set of customers for a future DR program; providing energy savings rewards; and adjusting energy supplying techniques and energy supplying routes.
This invention relates to a computer-implemented method for analyzing and optimizing demand response (DR) programs in utility transmission and distribution systems. The method addresses the challenge of accurately assessing the effectiveness of energy-saving events and optimizing load management by leveraging multi-level analytics data. The system receives analytics data at three hierarchical levels: feeder, transformer, and customer. For each level, a confidence score is generated. The feeder-level confidence score evaluates the potential cost savings from reducing energy transmission. The transformer-level score builds on the feeder score and assesses the transformer's role in energy distribution. Customer-level scores estimate individual energy savings during DR events. These scores collectively determine the overall effectiveness of the DR program, including energy and cost savings. The method further uses these confidence scores to purchase energy, select target customers for future DR programs, distribute energy savings rewards, and adjust energy supply techniques and routes. The output includes detailed information on confidence scores and DR program performance, enabling utilities to refine load management strategies and improve efficiency. The approach ensures data-driven decision-making for optimizing energy distribution and demand response initiatives.
2. The method of claim 1 , further comprising selecting a group of customers for participation in the DR program based on the confidence scores of the customers.
3. The method of claim 2 , further comprising sending a message to the selected customers to notify the selected customers of the DR program.
4. The method of claim 1 , further comprising determining, based on the confidence scores of the customers, rewards for reducing energy consumption.
This invention relates to energy management systems that optimize energy consumption by analyzing customer behavior and providing incentives. The system monitors energy usage patterns of multiple customers and assigns confidence scores to each customer based on their likelihood of reducing energy consumption. These confidence scores are used to determine rewards for customers who successfully reduce their energy usage. The rewards are designed to encourage energy conservation by providing financial or other incentives to customers who demonstrate a high likelihood of reducing consumption. The system may also track historical energy usage data to refine the confidence scores and improve the accuracy of reward distribution. By dynamically adjusting rewards based on customer behavior, the system promotes efficient energy use and helps balance energy demand. The invention aims to address the challenge of managing energy consumption in a way that aligns with both customer incentives and overall energy efficiency goals.
5. The method of claim 1 , further comprising determining energy supply purchasing based on the confidence scores of the feeder, transformer, or the customers.
6. The method of claim 1 , further comprising: segmenting a plurality of feeders into clusters based on analytics factors for the feeders; and segmenting a plurality of transformers into clusters based on analytics factors for the transformers, wherein the feeder is included in a selected cluster of feeders and the transformer is included in a selected cluster of transformers, and the generating the confidence scores for the customers is based on the segmenting the plurality of feeders and the plurality of transformers into clusters.
The invention relates to a method for improving the reliability and efficiency of electrical power distribution systems by analyzing and clustering feeders and transformers based on analytics factors. The method addresses the challenge of managing large-scale power distribution networks by grouping similar feeders and transformers into clusters, which allows for more targeted and efficient monitoring, maintenance, and predictive analytics. The method involves segmenting a plurality of feeders into clusters based on analytics factors specific to each feeder, such as load patterns, historical outage data, or environmental conditions. Similarly, a plurality of transformers are segmented into clusters based on their respective analytics factors, which may include performance metrics, maintenance history, or operational conditions. A feeder and a transformer are then selected from their respective clusters, and confidence scores are generated for customers connected to these feeders and transformers. The confidence scores are derived from the clustering process, enabling more accurate predictions of customer reliability, potential failures, or maintenance needs. By clustering feeders and transformers, the method enhances the ability to identify patterns and anomalies within the power distribution system, leading to improved decision-making for grid management and customer service. This approach allows utilities to prioritize resources and interventions more effectively, reducing downtime and enhancing overall system reliability.
7. The method of claim 1 , wherein the analytics data for the feeder or transformer includes at least one of: cost per unit of energy; estimated revenue per customer category; transmission power loss ratios; load balancing data; and historical demand response program participation data.
This invention relates to an analytics system for monitoring and optimizing electrical power distribution networks, specifically focusing on feeders and transformers. The system collects and analyzes detailed operational data to improve efficiency, cost management, and reliability in power distribution. The analytics data includes key metrics such as cost per unit of energy, which helps assess financial performance. It also tracks estimated revenue per customer category, enabling better pricing strategies and revenue forecasting. Transmission power loss ratios are analyzed to identify inefficiencies in energy delivery, while load balancing data ensures optimal distribution across the network. Additionally, historical demand response program participation data is used to evaluate customer engagement in energy-saving initiatives. By integrating these metrics, the system provides actionable insights for utilities to reduce costs, minimize losses, and enhance grid stability. The data-driven approach supports decision-making in energy pricing, load management, and demand response programs, ultimately improving overall network performance. This solution addresses challenges in power distribution by leveraging real-time and historical data to optimize operations and financial outcomes.
8. The method of claim 1 , wherein the customer analytics data includes at least one of: number of incomes for in a customer household; total customer family income; number of family members in a customer household; customer profession; customer category or type; customer interests; number of appliances owned by a customer; historical customer DR program participation or rating; customer net energy metering (NEM) rating; customer registration status in participating in DR programs; customer participation environmental conservation programs; and customer participation in third party promotional programs.
This invention relates to customer analytics for demand response (DR) programs in energy management systems. The technology addresses the challenge of effectively targeting and engaging customers in DR programs by leveraging detailed customer data to improve program participation and energy efficiency outcomes. The method involves collecting and analyzing customer analytics data to assess eligibility, suitability, and potential impact for DR program participation. The data includes household income metrics (number of incomes, total family income), household demographics (number of family members, profession, customer category or type), lifestyle factors (interests, appliance ownership), historical engagement (DR program participation or ratings, net energy metering (NEM) ratings), and program registration status. Additionally, the data covers participation in environmental conservation programs and third-party promotional programs. By integrating these diverse data points, the system enables more precise customer segmentation and personalized DR program recommendations. This enhances program effectiveness by targeting customers most likely to benefit from or contribute to energy-saving initiatives, while also optimizing incentives and engagement strategies. The approach aims to improve energy efficiency, reduce peak demand, and foster sustainable energy consumption habits among participants.
9. The method of claim 1 , wherein the generating the customer confidence scores is based on ensemble modeling techniques.
The invention relates to a system for generating customer confidence scores using ensemble modeling techniques to assess the reliability of customer interactions or data. The method involves collecting interaction data from various sources, such as customer service logs, transaction records, or behavioral analytics, and processing this data to derive confidence metrics. These metrics evaluate the trustworthiness, accuracy, or consistency of customer-provided information or actions. The system employs ensemble modeling, which combines multiple predictive models to improve the robustness and accuracy of the confidence scores. This approach mitigates biases or errors that individual models might introduce. The generated scores can be used to prioritize customer service responses, detect fraudulent activities, or enhance decision-making processes in business operations. The method ensures that the confidence scores are dynamically updated as new interaction data becomes available, allowing for real-time adjustments. By leveraging ensemble modeling, the system provides a more reliable assessment of customer behavior compared to single-model approaches. The invention is particularly useful in industries where customer trust and data accuracy are critical, such as banking, e-commerce, or customer support services.
10. The method of claim 1 , wherein a utility service provider at least one of creates, maintains, deploys and supports the computing device.
A system and method for managing computing devices in a utility service environment involves a utility service provider that creates, maintains, deploys, or supports a computing device. The computing device is configured to monitor and control utility service delivery, such as electricity, water, or gas, by collecting data from utility meters or sensors and transmitting it to a central system. The device may also receive commands to adjust service delivery, such as remotely disconnecting or reconnecting a utility service. The utility service provider ensures the computing device operates reliably, performs updates, and provides technical support. This system enhances utility service management by automating data collection, improving service reliability, and enabling remote control of utility services. The computing device may be deployed at customer premises, utility substations, or other strategic locations to optimize service delivery and maintenance. The provider's involvement ensures seamless integration with existing utility infrastructure and compliance with regulatory requirements. This approach reduces manual intervention, minimizes service disruptions, and enhances overall efficiency in utility service operations.
11. The method of claim 1 , further comprising deploying a system for determining effectiveness of DR programs and selecting a group of customers for participation in a DR program, comprising providing a computer infrastructure operable to perform the steps of claim 1 .
12. The method of claim 11 , wherein the customer analytics data includes: number of incomes for in a customer household; total customer family income; number of family members in a customer household; customer profession; customer category or type; customer interests; number of appliances owned by a customer; historical customer DR program participation or rating; customer net energy metering (NEM) rating; customer registration status in participating in DR programs; customer participation environmental conservation programs; and customer participation in third party promotional programs.
This invention relates to customer analytics for demand response (DR) programs in energy management systems. The technology addresses the challenge of effectively targeting and engaging customers in energy conservation initiatives by leveraging detailed household and behavioral data. The method collects and analyzes comprehensive customer analytics data to optimize DR program participation and energy efficiency strategies. The analytics data includes key household metrics such as the number of income sources, total family income, and the number of family members. It also captures professional and demographic details like customer profession, category or type, and interests. The system further tracks appliance ownership, historical DR program participation ratings, and net energy metering (NEM) ratings. Additionally, it monitors customer registration status in DR programs, participation in environmental conservation programs, and involvement in third-party promotional programs. By integrating these diverse data points, the system enables energy providers to tailor DR programs more effectively, improving customer engagement and energy conservation outcomes. The detailed analytics help identify high-potential participants, optimize incentive structures, and enhance the overall efficiency of demand response initiatives. This approach ensures that energy management strategies are data-driven and aligned with customer behavior and preferences.
13. The method of claim 12 , further comprising: generating, by the computing device, an impact on the DR program in view of the customers participating in the DR program based on the confidence scores for the customers; and generating, by the computing device, energy and cost savings in view of the impact.
This invention relates to demand response (DR) programs in energy management, specifically improving the accuracy and impact assessment of customer participation. The technology addresses the challenge of predicting and quantifying the effectiveness of DR programs by analyzing customer behavior and participation confidence. The method involves evaluating customer participation in a DR program using confidence scores, which reflect the likelihood of a customer adhering to the program's requirements. These scores are derived from historical data, real-time usage patterns, and other relevant factors. The system then assesses the overall impact of the DR program by aggregating the confidence scores of participating customers, providing a measurable indication of program success. Additionally, the method calculates energy and cost savings based on the program's impact. By correlating participation confidence with energy reduction outcomes, the system quantifies the financial and operational benefits of the DR program. This enables utilities and energy providers to optimize program design, target high-confidence participants, and enhance overall efficiency. The invention improves upon existing DR program management by introducing a data-driven approach to participation prediction and impact analysis, leading to more accurate forecasting and better resource allocation.
14. A computer program product for determining an effectiveness of a demand response (DR) program, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to: determine an effectiveness of the demand response (DR) program based on analytics information of a hierarchy of a utility transmission and distribution system, and analytics information of customers associated with the utility transmission and distribution system, wherein the analytics information of the hierarchy of a utility transmission and distribution system and the analytics information of the customers is obtained using an application program interface (API), wherein the DR program is an energy savings event for utility load management; determine confidence scores of the customers based on the analytics information of the hierarchy of a utility transmission and distribution system and the analytics information of the customers associated with the utility transmission and distribution system; identify alternate routes for supplying the customers with electricity by using the confidence scores of the customers to identify feeders and transformers having lower power loss ratios and cost per unit in supplying electricity to the customers; purchase energy based on the confidence scores; and determine and select a target set of customers for which to select to participate in the DR program; wherein the confidence scores of the customers indicate an estimated amount of energy saved by the customers during the DR program.
This invention relates to evaluating and optimizing demand response (DR) programs in utility transmission and distribution systems. The technology addresses the challenge of accurately assessing the effectiveness of energy-saving events while improving load management and reducing costs. The system uses analytics data from both the utility infrastructure (e.g., transmission and distribution hierarchy) and customer consumption patterns, obtained via an application programming interface (API). By analyzing this data, the system calculates confidence scores for customers, representing their estimated energy savings during DR events. These scores help identify optimal routes for electricity distribution, prioritizing feeders and transformers with lower power loss ratios and cost per unit. The system also uses the confidence scores to purchase energy efficiently and select a targeted subset of customers for DR participation, ensuring cost-effective and reliable load management. The approach enhances grid efficiency, reduces operational costs, and improves the reliability of DR programs.
15. The computer program product of claim 14 , wherein the analytics information of the hierarchy indicates a potential cost savings for reducing energy transmitted via the utility transmission and distribution system.
The invention relates to energy management systems that analyze utility transmission and distribution networks to identify cost-saving opportunities. The system collects data from sensors and meters across the network, including voltage levels, power flow, and equipment status, to model the electrical grid's performance. Using this data, the system generates analytics that highlight inefficiencies, such as excessive energy losses or suboptimal power routing, which could be mitigated to reduce operational costs. The analytics may include visualizations, reports, or alerts that guide operators in adjusting transmission paths, optimizing load distribution, or implementing energy-saving measures. By identifying areas where energy transmission can be reduced without compromising service reliability, the system helps utilities and grid operators minimize waste and lower expenses. The invention may also integrate with demand response programs or renewable energy sources to further enhance cost efficiency. The goal is to provide actionable insights that lead to measurable financial savings while maintaining grid stability.
16. The computer program product of claim 14 , wherein the program instructions further cause the computing device to send a message to the target set of customers to notify the target set of customers of the DR program.
This invention relates to demand response (DR) programs in energy management systems, specifically addressing the need to efficiently notify and engage target customer groups about DR initiatives. The system uses a computing device to analyze customer data, such as energy consumption patterns, preferences, and historical participation, to identify a target set of customers likely to benefit from or contribute to a DR program. The computing device then generates a notification message tailored to this group, which may include details about the program, incentives, or participation requirements. The message is sent to the target customers via one or more communication channels, such as email, SMS, or in-app notifications, to ensure timely and relevant engagement. The system may also track message delivery and customer responses to optimize future notifications. This approach improves participation rates in DR programs by leveraging data-driven targeting and personalized communication, ultimately enhancing grid stability and energy efficiency. The invention may be implemented as part of a broader energy management platform or as a standalone notification system for utility companies or energy service providers.
17. The computer program product of claim 14 , wherein the program instructions further cause the computing device to determine, based on the confidence scores of the customers, rewards for reducing energy consumption.
This invention relates to energy management systems that use customer behavior data to optimize energy consumption and incentivize conservation. The system analyzes customer energy usage patterns and assigns confidence scores to predict future consumption behavior. These scores are used to determine personalized rewards for customers who reduce their energy usage, encouraging more efficient energy consumption. The system may also track historical usage data, compare it to current behavior, and adjust rewards dynamically based on the likelihood of sustained energy savings. By providing targeted incentives, the system aims to reduce overall energy demand while maintaining customer satisfaction. The invention improves upon traditional energy management approaches by incorporating predictive analytics and behavioral insights to create a more adaptive and effective conservation program.
18. A system comprising: a CPU, a computer readable memory and a computer readable storage medium associated with a computing device; program instructions to receive information regarding confidence scores associated with a feeder, a transformer associated with the feeder, and customers associated with the transformer, wherein the confidence scores for the transformer is based on the confidence scores for the feeder, and the confidence scores of the customers are based on the confidence score for the transformers, wherein the information is received via an application programming interface (API); program instructions to purchase energy based on the confidence scores; program instructions to identify alternate routes for supplying the customers with electricity by using the confidence scores of the customers to identify feeders and transformers having lower power loss ratios and cost per unit in supplying electricity to the customers; program instructions to select a particular group of the customers for participation in a demand response (DR) program based on the confidence scores of the customers, wherein the DR program is an energy savings event for utility load management; program instructions to determine and provide rewards for the customers as part of the DR program; program instructions to implement the DR program based on the confidence scores of the customers; and program instructions to output a message to the group of customers to notify the customers regarding the selection of the group of customers for the participation in the DR program; wherein the confidence scores of the customers indicate an estimated amount of energy saved by the customers during the DR program, and wherein the program instructions are stored on the computer readable storage medium for execution by the CPU via the computer readable memory.
The system is designed for optimizing energy distribution and demand response (DR) management in electrical power grids. It addresses challenges in accurately assessing energy savings, reducing power losses, and efficiently managing customer participation in DR programs. The system uses confidence scores to evaluate the reliability and efficiency of feeders, transformers, and individual customers. These scores are hierarchically derived, where transformer confidence scores depend on feeder scores, and customer scores depend on transformer scores. The system receives this data via an API and leverages it to purchase energy, identify optimal power supply routes with lower loss ratios and costs, and select customers for DR programs. It also determines rewards for participating customers and implements the DR program while notifying selected participants. The confidence scores estimate the energy savings potential of customers during DR events, ensuring targeted and effective load management. The system operates on a computing device with a CPU, memory, and storage medium, executing program instructions to perform these functions.
19. The system of claim 18 , further comprising: program instructions to determine potentially an estimated amount of energy to purchase based on the confidence scores of the feeder, the transformer, or the customers.
The system relates to energy management in electrical distribution networks, specifically addressing the challenge of optimizing energy procurement decisions based on predictive analytics. The system includes program instructions to analyze data from electrical feeders, transformers, and customer loads to generate confidence scores, which indicate the reliability or accuracy of predictions related to energy demand or supply. These confidence scores are used to estimate the amount of energy that should be purchased to meet future demand while minimizing costs and ensuring grid stability. The system further includes program instructions to determine an estimated amount of energy to purchase based on these confidence scores, allowing for more informed and adaptive energy procurement decisions. By integrating predictive analytics with real-time monitoring of grid components, the system helps utilities and energy providers optimize energy purchases, reduce waste, and improve overall grid efficiency. The system may also include additional features such as data collection from sensors, historical demand analysis, and integration with external energy markets to enhance the accuracy of energy procurement decisions.
Unknown
November 10, 2020
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